Study on hybrid pattern recognition algorithm for modulated signals

Abstract

A combined feature extraction and recognition method is proposed based on higher-order spectrum, cyclic spectrum and time-frequency characteristics. In the application of this method, α-dimensional features, quadratic spectral characteristics and Fourier transform spectral characteristics of the signal are used to extract three characteristic values including the envelope means (EM) of α plane, the recursive normalized frequency component detection value (RNFCDV) and the quadratic spectrum normalized frequency component detection value (QSNFCDV), which have the merits of less identification parameters, insensitive to noise, less computation, high recognition rate, and multi-species identification. With this method, simulation results show that the recognition rate is more the 98% with the signal to noise rate (SNR) not less than 6 dB. And the performance of this method is better than the common recognition algorithms. There are eight types of signal, such as amplitude modulation (AM), phase modulation (PM), amplitude shift keying (ASK), frequency shift keying (FSK), phase shift keying (PSK), minimum shift keying (MSK), quadrature amplitude modulation (QAM) and direct sequence spread spectrum (DSSS), have been used to validate the feasibility of the method.